I met a slightly creepy yet really interesting company yesterday.  Voiceprism has a technology that analyzes sound waves generated by the human voice.  This gives them the ability to listen to an individual for 10 minutes, establish a baseline voice pattern and then detect the deviations from the normal frequencies.  These deviations have proven to be indicative of emotions such as excitement, stress, delight, anger, …

This reminded me of the biometrics Innerscope looks at to gauge emotional engagement.  The voice could be an additional data point that can give us clues about what is happening in the subconscious.  The main differentiator for this technology is its ability to listen in on thousands of phone conversations.  This means it can be used at scale in a cost effective way.  I haven’t seen another technology that has the potential to capture data from the subconscious for potentially thousands of consumers.

They talked to me about their work with insurance companies where they analyze claims calls and flag insurance claims that should be investigated further based on the stress levels of the callers.  They were basically doing automated lie detection.  This apparently helped insurance companies dramatically reduce fraud levels. 

I think there could be other interesting applications, especially in quantitative research where it is often hard to quantify true emotional engagement through analyzing what people are saying.  How they are saying it can now apparently give us valuable clues as well.  Click here to see a video of how the technology works.

In my previous post I listed some empirical evidence that shows that TV advertising is becoming increasingly effective. One of the reasons is that TV is a superior medium for driving emotional engagement. The role of emotional engagement in driving purchasing behavior has been hotly debated in the last couple of years. This is as a result of advances in neuroscience that have demonstrated that the decisions people make are often a result of what happens in their sub-consciousness. As a result the traditional linear AIDA model, where the role of communications and advertising is to move customers down a linear path from awareness to interest to desire to action by grabbing their attention, has been severely challenged. 

 

 

There is an increasing amount of empirical evidence that suggests TV advertising is getting increasingly effective.  Perhaps the most often quoted analysis is the one done by Les Binet and Peter Field in 2008.  They mined the IPA databank which holds more than 1000 marketing effectiveness case studies from around the world gathered as part of the IPA Marketing Effectiveness Awards.  They found that campaigns that used TV have been growing in effectiveness over the past 3 decades.  The graph below shows the average % increase in market share growth for campaigns that include TV for the 1980’s, 1990’s and 2000’s.

In June 2009 Joel Rubinson of the ARF performed one of the most extensive reviews of quantitative evidence available in an attempt to validate the hypothesis that TV is losing its effectiveness.  He combined 7 different databases of quantitative evidence of the impact of TV campaigns on sales.  They included data from the IRI panel, econometric modeling companies like PM Group, Dratfield and Marketing Evolution and research data from Pointlogic and Milward Brown.  This combined database contained more than 300 cases across 74 different product categories.  Rubinson was not able to validate the hypothesis that TV is becoming less effective.  On the contrary – 6 out of the 7 databases showed that TV advertising has become more effective over time.  The Pointlogic data for example showed that among 25 different types of touchpoints measured in their survey between 2004 and 2007, TV moved up from 7^th to 4^th in terms of people impacted per $1,000 spent.  In an interview with Brandweek Rubinson said that his study concluded that “units sold numbers increased as a result of increased TV impressions. [When you observe it] across 388 case histories, I think you’ve got to believe it.

These findings are also confirmed by marketing science company MMA.  “We haven’t seen a significant trend in the erosion of effectiveness of TV,” said Douglas Brooks, senior VP of MMA in an interview with Advertising Age in Feb 2009. In fact, MMA, which reports to clients each year on its findings regarding aggregate TV effectiveness, has seen a slight uptick in effectiveness in recent years.

This is a bit of a geeky post so apologies in advance.  But I have been meaning to write about how fantastic I think Google Analytics Intelligence is.  If you are not sure what I am talking about have a look at the video below.

People have been buying and selling data about consumers for a long time.  Companies like Axciom have been doing this for years in the direct marketing business.  But recently a new breed of companies has been popping up who are acquiring and selling data about consumers in the online advertising space.  Companies like BlueKai and Datran are the modern day, digital equivalent of the Axcioms. The good old direct marketing techniques from the 80’s and 90’s are now also being used for targeting online advertising ads.  Therefore data collected about consumers can now be used for smarter targeting across all direct and digital channels.  This broader playing field will dramatically grow the size of the data reselling business in the next few years. 

So with so many companies buying and selling data about consumers, what really determines the value (and therefore the price) of a data point?  I think there are 3 drivers.

1. Predictive Power

The 1^st driver is Predictive Power of the data point.  Let’s say for example that I am a manufacturer of drills and that I am trying to purchase data points that will help me identify whether a consumer is interested in buying a drill.  And let’s assume that I can choose between the following 2 sets of data points.

Most people would agree that the data points in set 1 are more valuable for a drill manufacturer than those in set 2.  This is because of their natural correlation with someone’s likelihood to purchase drills.  This example is very straightforward.  If you had to determine the Predictive Power of a 100 different data points however, you would have to build statistical models that predict the likelihood of someone buying a drill based on all 100 data points.  Those that enter the model have a high Predictive Power which can be quantified by the lift they generate in the models.  Whether you build statistical models or not, the principle is that data points with a high Predictive Power will improve our prediction of whether a consumer will be interested in buying a drill and, as a drill manufacturer, I am prepared to pay a higher price for them.

2. Recency

The 2^nd driver is Recency.  This is really a special case of Predictive Power but I want to call it out separately as it has become an increasingly important driver.  In a digital world people often reveal real time what their intentions are.  Knowing whether a person has searched for drills on Google, whether they have clicked on a banner for drills or whether they have seen a drill related video online can be very powerful.  These data points generally outperform the more traditional data points that are listed in the example above because they are direct indications of a consumer’s interests and needs at a certain point in time.  For these self disclosed data points, Recency is very important.  When someone searches for a drill on Google then that is very valuable information if I can target that person immediately.  However, if I know that someone searched for a drill 3 months ago then that single observation in itself is a lot less valuable.  The predictive power of self disclosed data points starts to decline minutes after the observed event.  Because of the disproportionately high value of very recent data we anticipate most of the future innovation to focus on capturing multiple events real time and shortening the cycles between observed events and the ability to use that knowledge for targeting.  This is already happening on advertising exchanges through the introduction of Real Time Buying. 

3. Exclusivity

The final driver is Exclusivity.  Let’s use the same example and let’s assume that I can only buy the data points in set 1.  Let’s also assume that I have built a statistical model and have determined that the general predictive power of the number of hammers a consumer owns is far more predictive than the other 2 data points.  I would be prepared to pay a relatively high price for data on hammer ownership.  Now consider an alternative scenario where one additional data point is available: the number of nails a person uses per year.  Let’s assume that the general predictive power of nails consumption is almost as high as that of hammer ownership.  The availability of nails consumption will have an effect on the price I am prepared to pay for hammer ownership.  It’s the basic laws of supply and demand.

In the next few years the buying and selling of data will undoubtedly become a lot more streamlined.  When that happens, the market drivers described above will increasingly determine the price companies are willing to pay for information about their consumers.  Consumers on the other hand will get a much more transparent view of the value they are generating by allowing companies to collect their data.  Who knows, maybe they’ll even be able to claim their share of the pie.

It seems like every digital marketer and agency is talking about the power of listening to your consumers in the digital space.  This is fuelled by an entire industry of online listening technology providers that have popped up over the last few years.  They all promise the ability to mine the incredible wealth of information available on blogs and social networks to generate insights about what consumers talk about, what’s on their minds and what language they use to talk about the brand or issues related to the brand.  While the technological possibilities seem limitless I have still only seen a handful of clients use the information to actually improve their communications.  Earlier this week, Emily Steel from the Wall Street Journal wrote about how Ogilvy helped IBM and Harrah’s do this.  

I would just like to spend 2 minutes on the Harrah’s case study for 2 reasons.  First I would like to give the credit for the work to Jim Dravillas and his team.  They have really done tremendously inventive work for Harrah’s over the years.  Sorry Jim – in terms of recognition I realize the doublethink isn’t quite the Wall Street Journal!

The other thing I want to point out is how the Harrah’s example shows that action is more important than listening.  The Harrah’s analysis was surprisingly simple.  While we have used online listening technology in the past, in this case Jim and his team simply read the top 50 tripadvisor.com ratings and categorized the topics they saw using common sense.  My grandmother could have done this (no disrespect to Jim and the team – or my grandmother).  What was brilliant about this case however was that the team came up with clear and simple recommendations upon which they acted by changing the communications.   It’s this last step – taking action – which is always the hardest.  Listening is easy.

This tool turns YouTube into a phenomenal source of insights.